M.J. Kirkby et al. Agriculture, Ecosystems and Environment 81 2000 125–135 131
Integration of the storm runoff and sediment trans- port equations can be carried out analytically over this
distribution to give the form of the erosion risk ex- pression. The overall effectiveness of the erosion risk
estimates may be compared with observed empirical relationships between sediment loss and climate, with
a minimum in temperate zones. A model compari- son Kirkby, 1995 with a transect across the southern
United States shows fair qualitative agreement with empirical summaries Langbein and Schumm, 1958.
3. Results and discussion
3.1. Applications to erosion risk in France A preliminary attempt has been made to imple-
ment these methods for France, based on data held by INRA, Orléans, and which has already been used to
prepare a preliminary qualitative assessment Montier et al., 1998. Parameters have been derived from the
CORINE land cover survey, a 5 km gridded database for interpolated monthly mean rainfall totals, a 250 m
resolution DEM and information from the draft Eu- ropean Geographic Soils Database. These have been
used to create qualitative pedo-transfer functions, based primarily on INRA experience, to estimate
monthly vegetation cover, runoff thresholds, crusting class and soil erodibility. A preliminary map, show-
ing the feasibility of the approach, is shown in Fig. 6 Yassoglou and Jones, 1998; Kirkby et al., 1998, but
the results are, to date, not fully validated.
This exercise demonstrates that these methods are able to lead to reasonable estimates of monthly average
erosion rates, but that there is considerable scope for improvement in parameterisation from existing data
sources, the creation of additional special purpose data layers within European Databases, and for a substan-
tial exercise in validation at a range of appropriate scales.
This work has been completed, using the meth- ods set out above, generating monthly and annual
estimates of erosion risk. These are presented in cat- egories, which are provisionally classified as mean
erosion loss rates. At present pedo-transfer rules have been based on the experience of Le Bissonais
and other colleagues at INRA, Orléans, but the final product is un-validated at this stage. Although the
resolution shown is 250 m, it should also be recog- nised that less reliance should be made on individual
pixel values than on the clear regional patterns. Fore- casts are available for each month, and could also be
expressed in terms of the probability of exceedance of a given event size.
3.2. Data sources 3.2.1. Soils
The European Geographic Soils Database for France is clearly the best and main source for erodi-
bility and some of the soil storage terms. Following the results of Montier et al. 1998 and Le Bissonais et
al. 1996, the values were used for the trial run based on data in the European Soils Database Tables 1–3.
3.2.2. Land cover The CORINE land cover map has been used. Stan-
dard functional conversions were used to convert cover types to vegetation cover Table 4.
Land cover classes were taken from the CORINE database. Additional information for types of arable
farming were taken from the table of Petits Régions Agricoles PRA crop returns for the same year, linked
Table 1 Erodibility classes from categories in European soils database
Relative erodibility class
a
Value Level of
confidence RMS
error Weak
1 Weak
± 80
Moderate 3
Moderate ±
50 Strong
10 Strong
± 20
a
The relative erodibility class is taken directly from the pedo- transfer functions in Montier et al., 1998.
Table 2 Crusting classes from categories in European soils database
Relative crusting class
a
Crust storage
b
, h
C
mm Level of
confidence RMS
error None
na Weak
12 Weak
± 80
Moderate 6
Moderate ±
50 Strong
2 Strong
± 20
a
The relative crusting class was taken directly from the classes ‘Battance’ in Montier et al., 1998.
b
The crust storage is greater than the total soil storage, then it is replaced by the soil storage, h
s
+ h
R
.
132 M.J. Kirkby et al. Agriculture, Ecosystems and Environment 81 2000 125–135
Fig. 6. Preliminary erosion risk map of France, at 250 m resolution.
Table 3 Water retention estimates from categories in European soils database
Soil texture class
a
Water retention mm at saturation
Water retention mm at field capacity 50 cm
Soil storage, h
S
mm Coarse
403 294
109 Medium
439 379
60 Medium fine
430 406
24 Fine
520 472
48 Very fine
614 567
47 Organic soils
766 708
58
a
Soil texture class was taken from the soil database. The values have been provided Christine Le Bas, personal communication from an experimental pedo-transfer function.
M.J. Kirkby et al. Agriculture, Ecosystems and Environment 81 2000 125–135 133
Table 4 Land cover types from CORINE classification in ESDB
Land cover
type
a
Initial surface storage, h
R
, mm Reduction
per month Arable
10 50
Other 5
a
Land cover type was also taken from the CORINE database. Arable storage was set to its initial value at times of cultivation
taken as seed-time and harvest. Table 5
Interception as percentage of storm rainfall estimated from ESDB Land cover type
Interception storage, h
I
, as of storm rainfall
Arable 5
Pasture, vineyards and tree crops 10
Forest 20
Heterogeneous 10
Natural degraded land 5
Urban, rock, wetlands na
to the database layer which identifies the PRA of each cell. This gave the proportions of winter and spring
sown arable, together with a total arable. If there were areas with both categories of arable, subtraction gave
an estimate of the area with both types in the same year. This provided estimates of initial surface storage
Table 4, interception Table 5 and vegetation cover for each month of the year Table 6.
3.2.3. Topography Relief was estimated as the standard deviation of re-
lief at each point in a DEM, based on all points within
Table 6 Land cover type from CORINE database within ESDB
Land cover type January
February March
April May
June July
August September
October November
December Arable
Winter sown 10
20 40
60 80
100 100
50 10
Spring sown 10
10 10
20 50
80 100
100 50
10 10
Both in 1 year 10
10 10
20 50
80 100
100 50
10 Pasture
100 100
100 100
100 100
100 100
100 100
100 100
Permanent vineyards, tree crops, etc.
30 30
30 40
50 60
60 60
60 40
30 30
Forest 100
100 100
100 100
100 100
100 100
100 100
100 Heterogeneous
50 50
50 60
70 80
90 90
60 50
45 45
Natural degraded 20
20 20
20 20
20 20
20 20
20 20
20 Rock, urban,
wetlands, etc. na
na na
na na
na na
na na
na na
na
a given radius. Test show that this is not sensitive to the DEM resolution, although the radius used is limited to
requiring at least five points in the sample i.e mini- mum radius≥DEM cell size. A radius of 1 km worked
well, and was satisfactory using the 250 m DEM for France available, with permission, through INRA.
3.2.4. Climate The 5 km interpolated rainfall map for France gave
an excellent quality for monthly mean rainfalls. In addition the table for ≈90 stations in France gave the
frequency of 24 h totals which were used to obtain the frequency distribution of daily rainfalls. Values of
the mean rain per rain day and its standard deviation were then assigned back to the 5 km grid to fit the
distributions of monthly rainfall intensities.
3.2.5. List of data required All of these are held by INRA for France, and are
subject to permission for use in the proposed context Table 7. They comprise a series of data layers from
the soils database at 250 m resolution, monthly pre- cipitation at 5 km resolution, additional tables of Pe-
tits regions Agricoles PRA and station climatic data. These data are then processed using a series of Arc
Macro Language AML algorithms.
3.3. Comparison with the INRA erosion risk map of France
Given the time available, there has been no statis- tical analysis of differences from the INRA study,
but only a visual comparison. The pattern of seasonal differences look realistic, and some areas show good
134 M.J. Kirkby et al. Agriculture, Ecosystems and Environment 81 2000 125–135
Table 7 Data required for LQI erosion model
Layers in soil database at 250 m resolution 1. Erodibility classes and reliability 2 layers
2. Crusting classes and reliability 2 layers 3. Soil texture classes
4. CORINE land use classes 5. PRA membership
6. Elevation DEMMNT 7. Slope classes for comparison only
Data layers at 5 km resolution Monthly mean precipitation 12 layers
Tables 1. PRA: Partition between crop classes esp. winter and spring sown arable
2. Climate data for 90 stations. Distribution of intensities for 24 h rainfall by month if available AlgorithmProgram
Interpolation routine used to apply metro station data to 5 km grid. The complete set of rules and algorithms was applied through macros AML in Arc-Info to obtain the final maps for average erosion risk in each month, as an estimated mean erosional loss. The annual map
is the sum of the 12 months
local and regional convergence with the INRA maps, but there are also divergences. For example, the Laura-
gais SE of Toulouse is well known for erosion prob- lems, which are better identified in the RDI than in the
INRA map. However, in other areas the RDI model seems to overestimate erosion, such as in central Bre-
tagne, between Rennes and Nantes, in Basse Nor- mandie between Caen and Granville, and in the Jura
area along the Swiss border. Many of these areas are almost completely covered by vegetation, grassland
and forest, respectively. In the initial runs, the whole of south and east France generally also showed too high
an erosion rate, and this contrast has been reduced by modifying the relief factor in the RDI estimator. It is
planned to improve and validate the model properly within a Framework V research grant PESERA.
4. Conclusions
